Multi-agent ontological clustering as a tool for improving the efficiency of factoring decisions

The paper proposes an innovative approach to managing factoring applications based on multi-agent ontological clustering with a feedback mechanism. Unlike traditional clustering methods, the proposed approach takes into account not only the numerical parameters of applications but also their semantic proximity, defined using ontologies. The system is implemented through the interaction of autonomous application agents and cluster agents, between which a two-way message exchange with an extended negotiation protocol is carried out. This allows agents to adaptively join existing clusters, create new ones, or reorganize existing ones to maintain internal semantic homogeneity. A distinctive feature of the proposed method is the built-in mechanism for automatic adjustment of rejected applications by selecting the closest approved analogues within semantically homogeneous clusters. This significantly increases the adaptability and efficiency of decision-making in factoring systems. The comparison with classical clustering algorithms showed that the proposed approach surpasses them in terms of flexibility, noise resistance, and the ability to take into account semantic relationships between data. The proposed methodology opens up wide prospects for practical application in banking, insurance, and government systems, where not only the accuracy of data analysis is important, but also the possibility of justified recommendations for adjusting and improving applications.

1. Albi G., Herty M., Segala Ch. Robust Feedback Stabilization of Interacting Multi-agent Systems Under Uncertainty. Applied Mathematics & Optimization. 2024;89(1). https://doi.org/10.1007/s00245-023-10078-2

2. Cao J., Zhao Ch.Q., Chen X., et al. From First Draft to Final Insight: A Multi-Agent Approach for Feedback Generation. arXiv. URL: https://arxiv.org/abs/2505.04869 [Accessed 18th May 2025].

3. Skobelev P.O. Situation-Driven Decision Making and Multi-Agent Technology: Finding Solutions in Dialogue. Ontology of Designing. 2013;(2):26–48. (In Russ.).

4. Zhilyaev A.A. Ontology as a Tool for Creating Open Multi-Agent Resource Management Systems. Ontology of Designing. 2019;9(2):261–281. (In Russ.).

5. Grachev S., Skobelev P., Mayorov I., Simonova E. Adaptive Clustering Through Multi-Agent Technology: Development and Perspectives. Mathematics. 2020;8(10). https://doi.org/10.3390/math8101664

6. Andreev V.V., Volkhontsev D.V., Ivkushkin K.V., et al. Mul'tiagentnaya sistema izvlecheniya znanii. In: Primenenie iskusstvennogo intellekta v inzhenerii XIII: Trudy 3-ei Mezhdunarodnoi konferentsii po problemam upravleniya i modelirovaniya slozhnykh sistem, 04–09 September 2001, Samara, Russia. Samara: SNTs RAN; 2001. P. 206–212. (In Russ.).

7. Rzevski G., Skobelev P., Minakov I. Data mining. № GB2411015A. Espacenet. URL: https://worldwide.espacenet.com/patent/search/family/032011806/publication/GB2411015A?q=GB2411015A [Accessed 18th May 2025].

8. Boryaev R.O., Chuvakov A.V. Construction of an Ontology for a System to Support Factoring Decisions Based on Quantum Computing. Modern High Technologies. 2024;(5–2):269–275. (In Russ.). https://doi.org/10.17513/snt.40039

9. Chuvakov A., Boryaev R. Factoring Decision Support System Based on Optimized Quantum Algorithms QMC. Informatics and Automation. 2025;24(2):657–683. (In Russ.). https://doi.org/10.15622/ia.24.2.11

10. Chaimontree S., Atkinson K., Coenen F. A Multi-Agent Based Approach to Clustering: Harnessing the Power of Agents. In: Agents and Data Mining Interaction: 7th International Workshop, ADMI 2011, 02–06 May 2011, Taipei, Taiwan. Berlin, Heidelberg: Springer; 2012. P. 16–29. https://doi.org/10.1007/978-3-642-27609-5_3